README.md

Deep Prediction Hub

Overview

Welcome to Deep Prediction Hub, a Streamlit web application that provides two deep learning-based tasks: Sentiment Classification and Tumor Detection.

Tasks

1. Sentiment Classification This task involves classifying the sentiment of a given text into "Positive" or "Negative". Users can input a review, and the application provides the sentiment classification using various models.

2. Tumor Detection In Tumor Detection, users can upload an image, and the application uses a Convolutional Neural Network (CNN) model to determine if a tumor is present or not. Getting Started

Prerequisites

Python 3.6 or higher
Required packages: streamlit, numpy, cv2, PIL, tensorflow
Pre-trained models: PP.pkl, BP.pkl, DP.keras, RN.keras, LS.keras, CN.keras
Trained IMDb word index: Ensure the IMDb word index is available for sentiment classification.

Installation

Clone the repository: git clone https://github.com/yourusername/deep-prediction-hub.git

Usage

Access the application by opening the provided URL after running the Streamlit app.

Choose between "Sentiment Classification" and "Tumor Detection" tasks.

Sentiment Classification

Enter a review in the text area.
Select a model from the dropdown.
Click "Submit" and then "Classify Sentiment."

Tumor Detection

Upload an image using the file uploader.
Click "Detect Tumor" to perform tumor detection.

Models

Perceptron (PP.pkl): Perceptron-based sentiment classification model.
Backpropagation (BP.pkl): Backpropagation-based sentiment classification model.
DNN (DP.keras): Deep Neural Network sentiment classification model.
RNN (RN.keras): Recurrent Neural Network sentiment classification model.
LSTM (LS.keras): Long Short-Term Memory sentiment classification model.
CNN (CN.keras): Convolutional Neural Network tumor detection model.

Contributing

Feel free to contribute by opening issues or submitting pull requests. Please follow the contribution guidelines.